




"""simple docstring"""


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers import AutoModelForCausalLM






@require_torch
class    lowercase(	unittest.TestCase					):







 '''simple docstring'''





 def 					UpperCamelCase_  (							self:						Union[str, Any]						):




   '''simple docstring'''



   _snake_case				:						int								=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
   _snake_case				:						List[str]								=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(_UpperCAmelCase						)
   _snake_case				:						Optional[int]								=    -1

   _snake_case				:						int								=    ids_tensor((1, 5), vocab_size=model.config.vocab_size						).to(_UpperCAmelCase						)
   _snake_case				:						int								=    model.generate(_UpperCAmelCase, max_new_tokens=10, do_sample=_UpperCAmelCase						)
   _snake_case				:						Union[str, Any]								=    tokenizer.decode(greedy_ids[0]						)

   with CaptureStdout() as cs:
     _snake_case				:						Dict								=    TextStreamer(_UpperCAmelCase						)
     model.generate(_UpperCAmelCase, max_new_tokens=10, do_sample=_UpperCAmelCase, streamer=_UpperCAmelCase						)
   # The greedy text should be printed to stdout, except for the final "\n" in the streamer
   _snake_case				:						Dict								=    cs.out[:-1]

   self.assertEqual(_UpperCAmelCase, _UpperCAmelCase						)


 def 					UpperCamelCase_  (							self:						str						):




   '''simple docstring'''



   _snake_case				:						Any								=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
   _snake_case				:						List[str]								=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(_UpperCAmelCase						)
   _snake_case				:						List[Any]								=    -1

   _snake_case				:						str								=    ids_tensor((1, 5), vocab_size=model.config.vocab_size						).to(_UpperCAmelCase						)
   _snake_case				:						List[Any]								=    model.generate(_UpperCAmelCase, max_new_tokens=10, do_sample=_UpperCAmelCase						)
   _snake_case				:						Optional[Any]								=    tokenizer.decode(greedy_ids[0]						)

   _snake_case				:						List[str]								=    TextIteratorStreamer(_UpperCAmelCase						)
   _snake_case				:						Any								=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
   _snake_case				:						Tuple								=    Thread(target=model.generate, kwargs=_UpperCAmelCase						)
   thread.start()
   _snake_case				:						Union[str, Any]								=    """"""
   for new_text in streamer:
     streamer_text += new_text

   self.assertEqual(_UpperCAmelCase, _UpperCAmelCase						)


 def 					UpperCamelCase_  (							self:						List[str]						):




   '''simple docstring'''



   _snake_case				:						Any								=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
   _snake_case				:						Tuple								=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(_UpperCAmelCase						)
   _snake_case				:						str								=    -1

   _snake_case				:						int								=    ids_tensor((1, 5), vocab_size=model.config.vocab_size						).to(_UpperCAmelCase						)
   _snake_case				:						Optional[Any]								=    model.generate(_UpperCAmelCase, max_new_tokens=10, do_sample=_UpperCAmelCase						)
   _snake_case				:						Optional[Any]								=    greedy_ids[:, input_ids.shape[1] :]
   _snake_case				:						Any								=    tokenizer.decode(new_greedy_ids[0]						)

   with CaptureStdout() as cs:
     _snake_case				:						Dict								=    TextStreamer(_UpperCAmelCase, skip_prompt=_UpperCAmelCase						)
     model.generate(_UpperCAmelCase, max_new_tokens=10, do_sample=_UpperCAmelCase, streamer=_UpperCAmelCase						)
   # The greedy text should be printed to stdout, except for the final "\n" in the streamer
   _snake_case				:						List[str]								=    cs.out[:-1]

   self.assertEqual(_UpperCAmelCase, _UpperCAmelCase						)


 def 					UpperCamelCase_  (							self:						Optional[int]						):




   '''simple docstring'''



   _snake_case				:						List[Any]								=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
   _snake_case				:						int								=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(_UpperCAmelCase						)
   _snake_case				:						int								=    -1

   _snake_case				:						List[Any]								=    torch.ones((1, 5), device=_UpperCAmelCase						).long() * model.config.bos_token_id
   with CaptureStdout() as cs:
     _snake_case				:						Union[str, Any]								=    TextStreamer(_UpperCAmelCase, skip_special_tokens=_UpperCAmelCase						)
     model.generate(_UpperCAmelCase, max_new_tokens=1, do_sample=_UpperCAmelCase, streamer=_UpperCAmelCase						)

   # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
   # re-tokenized, must only contain one token
   _snake_case				:						Optional[Any]								=    cs.out[:-1]  # Remove the final "\n"
   _snake_case				:						Union[str, Any]								=    tokenizer(_UpperCAmelCase, return_tensors="""pt"""						)
   self.assertEqual(streamer_text_tokenized.input_ids.shape, (1, 1)						)


 def 					UpperCamelCase_  (							self:						Tuple						):




   '''simple docstring'''



   _snake_case				:						Any								=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
   _snake_case				:						Any								=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(_UpperCAmelCase						)
   _snake_case				:						Optional[Any]								=    -1

   _snake_case				:						Tuple								=    ids_tensor((1, 5), vocab_size=model.config.vocab_size						).to(_UpperCAmelCase						)
   _snake_case				:						str								=    TextIteratorStreamer(_UpperCAmelCase, timeout=0.001						)
   _snake_case				:						Optional[int]								=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
   _snake_case				:						Union[str, Any]								=    Thread(target=model.generate, kwargs=_UpperCAmelCase						)
   thread.start()

   # The streamer will timeout after 0.001 seconds, so an exception will be raised
   with self.assertRaises(_UpperCAmelCase						):
     _snake_case				:						Dict								=    """"""
     for new_text in streamer:
       streamer_text += new_text







'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




